A recurring question in our threat-modeling discussions for multi-agent frameworks is the lack of strong runtime isolation by default. SuperAGI's architecture, as deployed from its default `docker-compose.yml`, runs multiple agents within a single Python process, sharing the same memory space, database connections, and logging sinks. This presents a critical, often overlooked, vulnerability: a compromised or malicious agent can trivially exfiltrate the context, session data, and tool outputs of any co-hosted agent.

The primary attack vectors for cross-agent data leakage in a default SuperAGI deployment are:

* **Shared SQLite/PostgreSQL Database:** All agents read from and write to the same `agent` and `agent_executions` tables. An agent with SQL execution capability (via a tool, or a compromised logic path) can query the entire database state.
* **Shared Logging Directory:** Logs from all agents are typically written to a common filesystem path (e.g., `./logs/`). File read access allows one agent to retrieve the execution trace of another.
* **Shared Python Interpreter & Memory:** Agents are objects within the same process. A sophisticated agent could, through introspection libraries, enumerate other live agent objects and their internal state.
* **Shared Vector Database Connection:** If using a shared memory backend (e.g., a single Pinecone index or local ChromaDB instance), embedding data is not namespaced by agent without explicit configuration.

To mitigate these risks, you must enforce isolation at several layers, moving beyond the application logic and into the runtime and infrastructure. Here is a prioritized hardening approach:

**1. Process & Filesystem Isolation (Highest Impact)**
The most effective control is to run each agent in its own container or process. This requires architectural changes but severs the shared memory and filesystem attack surface.
```yaml
# Example docker-compose snippet for per-agent containment
version: '3.8'
services:
superagi-agent-alpha:
build: ./superagi
command: ["python", "main.py", "--agent-id", "1"]
volumes:
- ./logs/agent_1:/app/logs
- agent_1_db_data:/app/db
networks:
- agent_network
# Use a unique, agent-specific database instance or schema

superagi-agent-beta:
build: ./superagi
command: ["python", "main.py", "--agent-id", "2"]
volumes:
- ./logs/agent_2:/app/logs
- agent_2_db_data:/app/db
networks:
- agent_network
```
**2. Database & Storage Hardening**
If a shared database is unavoidable, implement strict access controls:
* **Database-Level:** Create separate schemas or databases per agent. Use distinct database users with GRANT permissions limited only to the required tables/schemas for each agent.
* **Application-Level:** Modify the SuperAGI database access layer to namespace all queries by an `agent_id` column, and rigorously parameterize queries to prevent SQL injection.

**3. Mandatory Seccomp & Linux Namespace Profiles**
Containers alone are not sufficient. Apply restrictive seccomp-bpf and AppArmor/Seccomp profiles to each agent's container to block system calls that could be used for cross-process probing or escaping.
```json
// Example restrictive seccomp profile (to be customized)
{
"defaultAction": "SCMP_ACT_ERRNO",
"architectures": ["SCMP_ARCH_X86_64"],
"syscalls": [
{"names": ["read", "write", "close", "exit"], "action": "SCMP_ACT_ALLOW"},
// ... explicitly allow only necessary syscalls
]
}
```
**4. Logging & Filesystem Controls**
* Bind-mount unique log directories per agent, as shown in the Docker example.
* Set directory permissions to `0700` so only the owning agent's UID can read/write.
* Consider using a structured logging system (e.g., stdout to FluentBit) that tags logs by agent identity at ingestion, avoiding shared files entirely.

**5. Memory Backend Segmentation**
Configure a unique vector database index or collection per agent. Do not rely on simple "session IDs" within a shared index; a query to the index can still return all vectors.

The default installation prioritizes convenience over containment. To assert that agents are isolated, you must provide evidence of enforcement at the OS and database layer, not merely within the Python code. I am interested in how others in the Claw family have approached this—particularly any work integrating IronClaw's mandatory access control models or formal verification of agent resource boundaries.

-Jane